The invention relates to electronic nose systems and more specifically to an olfactory classification device.
The mammalian nose has millions of olfactory neurons that are neurologically connected to the olfactory bulb in the brain. Each neuron has a single odor receptor that is stimulated by a specific set of odorants. Odor receptors are not unique, and recent research concludes that the mouse nose, for example, consists of only ˜1000 unique odor receptors. Although odor receptors are randomly distributed throughout the nose, common odor receptors unite in a common location of the olfaction bulb. This phenomenon could be responsible for acute sensitivity. Each odor receptor responds to a unique subset of odorants, and each odorant stimulates a unique subset of odor receptors. Each odorant stimulates a unique combination of odor receptors that form a signature, or prototype pattern, for scent.
Currently, electronic nose systems exist, but major problems include size, cost, speed, portability, and ease of use. The problem domain for the electronic nose is twofold. In the first realm, sensitive sensors are necessary to function as transducers that convert chemical signals into the electrical domain. In the second realm, sensor data needs to be processed for scent recognition in a complex environment. The sensor problem is largely solved and is not a focus of this invention. Some of the more successful transducers include metal oxide sensors, ISFET, polymer resonating sensors, and optical bead sensors. However, olfactory pattern recognition and classification remains a challenge, especially in a complex environment with many sensors contaminated by background noise.
Most electronic nose pattern classification techniques use neural networks or statistical data analysis to classify odors. For example, CHEMFET sensors are known to be used for chemical recognition. Prior art shows successfully creating a reinforcement neural network to differentiate between three volatile organic compounds. Genetic algorithms are also being employed with success. Other hybrid approaches also seem promising. Unfortunately, these algorithms require powerful offline computer resources, are limited to about sixteen sensor inputs, are sensitive to noise, must be trained offline, or only detect a small number of odors.
Artificial neural networks have proven to be a useful tool for olfactory pattern recognition; but most silicon-based implementations have been limited in scale due to inherent constraints on chip real estate and synapse routing. The invention presents a new spiking neural network approach to odorant learning and detection based on new learned information about the mammalian olfaction system. The present invention explores a new olfactory pattern classification technique based on a binary spiking neural network. Spiking networks are suited for the olfactory system because they are close approximations to the biological network being emulated. Further, the spiking network of the present invention is ideal for VLSI because it does not require multiplication and uses efficient signal transmission.